98 lines
2.9 KiB
Markdown
98 lines
2.9 KiB
Markdown
# Dimensions and shape
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## The goal
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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## [numpy.ndarray.shape](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.shape.html)
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```python
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ndarray.shape
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```
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> Tuple of array dimensions.
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>
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> The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.
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```python
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import numpy as np
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data = np.zeros((2, 4, 2, 7, 2))
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print(data.shape) # -> (2, 4, 2, 7, 2)
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```
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## Vanishing dimensions
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```python
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import numpy as np
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data = np.zeros((5, 3, 2))
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# All the same dimensionwise
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print(data.shape) # -> (5, 3, 2)
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print(data[:].shape) # -> (5, 3, 2)
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print(data[:, :, :].shape) # -> (5, 3, 2)
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print(data[...].shape) # -> (5, 3, 2)
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print(data[0, :, :].shape) # -> (3, 2)
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print(data[:, 0, :].shape) # -> (5, 2)
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print(data[:, :, 0].shape) # -> (5, 3)
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print(data[:, 0, 0].shape) # -> (5,)
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print(data[0, :, 0].shape) # -> (3,)
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print(data[0, 0, :].shape) # -> (2,)
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print(data[0, 0, 0].shape) # -> ()
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print(type(data[0, 0, 0])) # -> <class 'numpy.float64'>
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```
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### keepdims
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There are functions like
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```python
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ndarray.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
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ndarray.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)
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ndarray.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
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ndarray.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
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ndarray.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
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ndarray.argmax(axis=None, out=None, *, keepdims=False)
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ndarray.argmin(axis=None, out=None, *, keepdims=False)
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```
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that normally make one dimension vanish. However, often this type of functions have an argument **keepdims** that keeps this dimension alive.
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```python
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import numpy as np
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data = np.zeros((5, 3, 2))
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# All the same dimensionwise
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print(data.shape) # -> (5, 3, 2)
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print(data.sum().shape) # -> ()
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print(data.sum(axis=0).shape) # -> (3, 2)
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print(data.sum(axis=1).shape) # -> (5, 2)
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print(data.sum(axis=2).shape) # -> (5, 3)
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# You can use keepdims:
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print(data.sum(axis=0, keepdims=True).shape) # -> (1, 3, 2)
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print(data.sum(axis=1, keepdims=True).shape) # -> (5, 1, 2)
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print(data.sum(axis=2, keepdims=True).shape) # -> (5, 3, 1)
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```
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As as reminder, shape is only availabe for np.ndarray and torch.Tensor matrices:
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```python
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z = int(7)
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print(np.array(z).shape) # -> ()
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print(type(np.array(z))) # -> <class 'numpy.ndarray'>
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print(type(z)) # -> <class 'int'>
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print(z.shape) # -> AttributeError: 'int' object has no attribute 'shape'
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```
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